用可旋转摄像头和持续学习实现无感长期追踪人类
Gimbal-Based Human Tracking for Companion Robots Using Continual Learning
- 通过机械云台动态调整视角，保持目标始终在视野内
- 实测提升追踪稳定性与连续性，支持从步行到跑步的实时识别
- 无需佩戴标签，适合家庭陪伴机器人长期使用
可靠且连续的人类追踪对自然的人机交互至关重要，尤其适用于陪伴机器人。然而，许多现有方法依赖可穿戴标签或视野有限的固定摄像头，导致系统灵活性差，当目标移出感知范围时常出现追踪失败。本文提出一种基于云台安装摄像头的追踪方法，集成于移动机器人中。通过主动控制云台机构，摄像头可动态调整视角，即使在机器人与人之间存在显著相对运动的情况下，也能保持目标在视场内。此外，针对人物重识别（ReID）任务采用持续学习策略，以适应长期追踪过程中外观和环境的变化。实验结果表明，所提系统显著提升了人类追踪的稳定性和连续性，实现了实时重识别，并能对从步行到跑步的人体运动提供响应式反馈。用户研究表明，该方法通过消除对可穿戴标签的需求，提升了用户体验舒适度。
原文摘要 · Abstract (English)
Reliable and continuous human tracking is essential for natural human-robot interaction, particularly for companion robots. However, many existing approaches rely on wearable tags or fixed cameras with limited fields of view, which reduces system flexibility and often causes tracking failures when the target moves outside the sensing range. In this paper, we present a human tracking approach based on a gimbal-mounted camera integrated into a mobile robot. By actively controlling the gimbal mechanism, the camera can dynamically adjust its viewing direction to maintain the target within the field of view, even under substantial relative motion between the robot and the human. Furthermore, a continual learning strategy is applied to the person re-identification (ReID) task to adapt to changes in appearance and environmental conditions during long-term tracking. Experimental results demonstrate that the proposed system significantly improves the stability and continuity of human tracking, enables real-time re-identification, and provides responsive feedback for reliable tracking of human motion from walking to running. User studies further indicate that the proposed approach enhances user comfort by eliminating the need for wearable tags.
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